Therapy optimization based on non-linear uptake of PET tracers versus "linear dose painting"
2009 (English)In: IFMBE Proceedings, Berlin: Springer , 2009, 221-224 p.Conference paper (Refereed)
Treatment optimization based on positron emission tomography (PET) images of tumor hypoxia has been proposed as a method to improve the cure rates in radiotherapy through the increased dose delivery to tumor regions with increased radioresistance. One of the major advantages of PET imaging of hypoxia is that it can provide information on both the extent and the spatial distribution of the resistant regions. One of the key issues for the practical implementation of this approach is the accurate conversion of the intensities in the recorded images into radiosensitivity maps that could then be used for dose escalation. The present paper explores the influence of the conversion from uptake to prescribed doses. Transformation functions derived from the uptake properties of the PET tracers were taken into consideration. The results have shown that the available tracers have different uptake properties and therefore they could interpret differently the gradients in the images which in turn would lead to different dose predictions. Best results in terms of dose prescription would therefore be achieved by carefully taking into account the uptake characteristics of the imaged tracers. Linear approximations could lead to unnecessary over-estimations of the doses for cases of partial hypoxia in tumors. This highlights the need for more experimental studies of the uptake properties of PET tracers proposed to image tissue hypoxia. These would eventually provide more reliable methods for dose prescription that could be used with optimization algorithms for the successful individualization of radiation therapy.
Place, publisher, year, edition, pages
Berlin: Springer , 2009. 221-224 p.
, IFMBE Proceedings, ISSN 1680-0737 ; 25/I
Hypoxia, treatment optimization, PET imaging
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:umu:diva-35104DOI: 10.1007/978-3-642-03474-9ISBN: 978-3-642-03472-5OAI: oai:DiVA.org:umu-35104DiVA: diva2:329352